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  Overview of compressed sensing: sensing model, reconstruction algorithm, and its applications

Li, L., Fang, Y., Liu, L., Peng, H., Kurths, J., Yang, Y. (2020): Overview of compressed sensing: sensing model, reconstruction algorithm, and its applications. - Applied Sciences, 10, 17, 5909.
https://doi.org/10.3390/app10175909

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 Creators:
Li, Lixiang1, Author
Fang, Yuan1, Author
Liu, Liwei1, Author
Peng, Haipeng1, Author
Kurths, Jürgen2, Author              
Yang, Yixian1, Author
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1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: With the development of intelligent networks such as the Internet of Things, network scales are becoming increasingly larger, and network environments increasingly complex, which brings a great challenge to network communication. The issues of energy-saving, transmission efficiency, and security were gradually highlighted. Compressed sensing (CS) helps to simultaneously solve those three problems in the communication of intelligent networks. In CS, fewer samples are required to reconstruct sparse or compressible signals, which breaks the restrict condition of a traditional Nyquist–Shannon sampling theorem. Here, we give an overview of recent CS studies, along the issues of sensing models, reconstruction algorithms, and their applications. First, we introduce several common sensing methods for CS, like sparse dictionary sensing, block-compressed sensing, and chaotic compressed sensing. We also present several state-of-the-art reconstruction algorithms of CS, including the convex optimization, greedy, and Bayesian algorithms. Lastly, we offer recommendation for broad CS applications, such as data compression, image processing, cryptography, and the reconstruction of complex networks. We discuss works related to CS technology and some CS essentials.

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 Dates: 2020-08-242020-08-262020
 Publication Status: Finally published
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.3390/app10175909
MDB-ID: No data to archive
Research topic keyword: Nonlinear Dynamics
Model / method: Nonlinear Data Analysis
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Working Group: Network- and machine-learning-based prediction of extreme events
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Title: Applied Sciences
Source Genre: Journal, SCI, Scopus, oa
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Pages: - Volume / Issue: 10 (17) Sequence Number: 5909 Start / End Page: - Identifier: Other: MDPI AG
Other: 2076-3417
CoNE: https://publications.pik-potsdam.de/cone/journals/resource/applied-sciences
Publisher: MDPI